NDLI: Rapid detection of significant spatial clusters

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Rapid detection of significant spatial clusters

Content Provider	ACM Digital Library
Author	Neill, Daniel B. Moore, Andrew W.
Abstract	Given an N x N grid of squares, where each square has a count cij and an underlying population pij, our goal is to find the rectangular region with the highest density, and to calculate its significance by randomization. An arbitrary density function D, dependent on a region's total count C and total population P, can be used. For example, if each count represents the number of disease cases occurring in that square, we can use Kulldorff's spatial scan statistic DK to find the most significant spatial disease cluster. A naive approach to finding the maximum density region requires $O(N^{4})$ time, and is generally computationally infeasible. We present a multiresolution algorithm which partitions the grid into overlapping regions using a novel overlap-kd tree data structure, bounds the maximum score of subregions contained in each region, and prunes regions which cannot contain the maximum density region. For sufficiently dense regions, this method finds the maximum density region in O((N log $N)^{2})$ time, in practice resulting in significant (20-2000x) speedups on both real and simulated datasets.
Starting Page	256
Ending Page	265
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014082
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cluster detection Biosurveillance Spatial scan statistics Spatial data mining algorithms
Content Type	Text
Resource Type	Article

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